The use of 5-fluorouracil-loaded nanobubbles combined with low-frequency ultrasound to treat hepatocellular carcinoma in nude mice

Abstract Objective This study aimed to investigate the therapeutic effects of 5-fluorouracil (5-FU)-loaded nanobubbles irradiated with low-intensity, low-frequency ultrasound in nude mice with hepatocellular carcinoma (HCC). Methods A transplanted tumor model of HCC in nude mice was established in 40 mice, which were then randomly divided equally into four groups: group A (saline), group B (5-FU-loaded nanobubbles), group C (5-FU-loaded nanobubbles with non-low-frequency ultrasound), and group D (5-FU-loaded nanobubbles with low-frequency ultrasound). The tumor size in each mouse was observed via ultrasound before and after the treatments. Inhibition of the tumor growth in each group was compared, and survival curves were generated. Tumor tissues were removed to determine the apoptotic index using the TUNEL method and quantitative analysis. Tumor tissues with CD34-positive microvessels were observed by immunohistochemistry, and the tumor microvessel densities were calculated. Results The growth rate of the tumor volumes in group D was significantly slower than that in the other groups, while the tumor inhibition rates and apoptotic index in group D were significantly higher than those of the other groups. The number of microvessels staining positive for CD34 was decreased in group D. Therefore, group D presented the most significant inhibitory effects. Conclusions Therefore, 5-FU-loaded nanobubbles subjected to irradiation with low-frequency ultrasound could further improve drug targeting and effectively inhibit the growth of transplanted tumors, which is expected to become an ideal drug carrier and targeted drug delivery system for the treatment of HCC in the future

Transition from Screw-Type to Edge-Type Misfit Dislocations at InGaN/GaN Heterointerfaces

We have investigated the interface dislocations in InxGa1−xN/GaN heterostructures (0 ≤ x ≤ 0.20) using diffraction contrast analysis in a transmission electron microscope. The results indicate that the structural properties of interface dislocations depend on the indium composition. For lower indium composition (up to x = 0.09), we observed that the screw-type dislocations and dislocation half-loops occurred at the interface, even though the former do not contribute toward elastic relaxation of the misfit strain in the InGaN layer. With the increase in indium composition (0.13 ≤ x ≤ 0.17), in addition to the network of screw-type dislocations, edge-type misfit dislocations were generated, with their density gradually increasing. For higher indium composition (0.18 ≤ x ≤ 0.20), all of the interface dislocations are transformed into a network of straight misfit dislocations along the direction, leading to partial relaxation of the InGaN epilayer. The presence of dislocation half-loops may be explained by a slip on basal plane; formation of edge-type misfit dislocations are attributed to punch-out mechanism

Neutron transport calculation for the BEAVRS core based on the LSTM neural network

Abstract With the rapid development of computer technology, artificial intelligence and big data technology have undergone a qualitative leap, permeating into various industries. In order to fully harness the role of artificial intelligence in the field of nuclear engineering, we propose to use the LSTM algorithm in deep learning to model the BEAVRS (Benchmark for Evaluation And Validation of Reactor Simulations) core first cycle loading. The BEAVRS core is simulated by DRAGON and DONJON, the training set and the test set are arranged in a sequential fashion according to the evolution of time, and the LSTM model is constructed by changing a number of hyperparameters. In addition to this, the training set and the test set are retained in a chronological order that is different from one another throughout the whole process. Additionally, there is a significant pattern that is followed when subsetting both the training set and the test set. This pattern applies to both sets. The steps in this design are very carefully arranged. The findings of the experiments suggest that the model can be altered by making use of the appropriate hyperparameters in such a way as to bring the maximum error of the effective multiplication factor keff prediction of the core within 2.5 pcm (10–5), and the average error within 0.5266 pcm, which validated the successful application of machine learning to transport equations

N–Doped Porous Carbon Microspheres Derived from Yeast as Lithium Sulfide Hosts for Advanced Lithium-Ion Batteries

Lithium sulfide (Li2S) is considered to be the best potential substitution for sulfur-based cathodes due to its high theoretical specific capacity (1166 mAh g−1) and good compatibility with lithium metal-free anodes. However, the electrical insulation nature of Li2S and severe shuttling of lithium polysulfides lead to poor rate capability and cycling stability. Confining Li2S into polar conductive porous carbon is regarded as a promising strategy to solve these problems. In this work, N-doped porous carbon microspheres (NPCMs) derived from yeasts are designed and synthesized as a host to confine Li2S. Nano Li2S is successfully entered into the NPCMs’ pores to form N-doped porous carbon microspheres–Li2S composite (NPCMs–Li2S) by a typical liquid infiltration–evaporation method. NPCMs–Li2S not only delivers a high initial discharge capacity of 1077 mAh g−1 at 0.2 A g−1, but also displays good rate capability of 198 mAh g−1 at 5.0 A g−1 and long-term lifespan over 500 cycles. The improved cycling and high-rate performance of NPCMs–Li2S can be attributed to the NPCMs’ host, realizing the strong fixation of LiPSs and enhancing the electron and charge conduction of Li2S in NPCMs–Li2S cathodes